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Data-driven analysis of process,structure,and properties of additively manufactured Inconel 718 thin walls

npj Computational Materials 2022年第1期8卷 1168-1182页

作者： Lichao Fang Lin Cheng jennifer a.glerum jennifer Bennett Jian Cao Gregory J.Wagner Department of Mechanical Engineering Northwestern UniversityEvanstonIL60208USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL60208USA DMG MORI Hoffman EstatesIL60192USA Department of Mechanical and Materials Engineering Worcester Polytechnic InstituteWorcesterMA01609USA Department of Civil and Mechanical Engineering US Military Acadeemy at West PointWest PointNY10996USA

In additive manufacturing of metal parts,the ability to accurately predict the extremely variable temperature field in detail,and relate it quantitatively to structure and properties,is a key step in predicting part performance and optimizing process *** this work,a finite element simulation of the directed energy deposition(DED)process is used to predict the space-and time-dependent temperature field during the multi-layer build process for Inconel 718 *** thermal model results show good agreement with dynamic infrared images captured in situ during the DED *** relationship between predicted cooling rate,microstructural features,and mechanical properties is examined,and cooling rate alone is found to be insufficient in giving quantitative property *** machine learning offers an efficient way to identify important features from series data,we apply a 1D convolutional neural network data-driven framework to automatically extract the dominant predictive features from simulated temperature *** good predictions of material properties,especially ultimate tensile strength,are obtained using simulated thermal history *** further interpret the convolutional neural network predictions,we visualize the extracted features produced on each convolutional layer and compare the convolutional neural network detected features of thermal histories for high and low ultimate tensile strength cases.A key result is the determination that thermal histories in both high and moderate temperature regimes affect material properties.

关键词： Inconel process additive

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Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing

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npj Computational Materials 2021年第1期7卷 767-778页

作者： Xiaoyu Xie jennifer Bennett Sourav Saha Ye Lu Jian Cao Wing Kam Liu Zhengtao Gan Department of Mechanical Engineering Northwestern UniversityEvanstonILUSA DMG MORI Hoffman EstatesILUSA Theoretical and Applied Mechanics Northwestern UniversityEvanstonILUSA

Metal additive manufacturing provides remarkable flexibility in geometry and component design,but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties,significantly complicating the materials design *** this end,we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperature sequences,i.e.,thermal *** framework enables multiresolution analysis and importance analysis to reveal dominant mechanistic features underlying the additive manufacturing process,such as critical temperature ranges and fundamental thermal *** systematically compare the developed approach with other machine learning *** results demonstrate that the developed approach achieves reasonably good predictive capability using a small amount of noisy experimental *** provides a concrete foundation for a revolutionary methodology that predicts spatial and temporal evolution of mechanical properties leveraging domain-specific knowledge and cutting-edge machine and deep learning technologies.

关键词： additive properties prediction

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